The biggest challenge facing the world in 2020 was the pandemic of the coronavirus disease (COVID-19). Since the start of 2020, COVID-19 has invaded the world, causing death to people and economic damage, which is cause for sadness and anxiety. Since the world has passed from the first peak with relative success, this should be evaluated by statistical analysis in preparation for potential further waves. Artificial neural networks and logistic regression models were used in this study, and some statistical indicators were extracted to shed light on this pandemic. WHO website data for 32 European countries from 11th of January 2020 to 29th of May 2020 was utilized. The rationale for choosing the stated methodological tools is that the classification accuracy rate of artificial neural networks is 85.6% while the classification accuracy rate of logistic regression models 80.8%.
In a different area of a field of the real life, problem of accurate
forecasting has acquired great importance that present the interesting serve
which led to the best ways to achieve a goal. So, in this paper, we aimed to
compare the accuracy of some statistical models such as Time Series and Deep
Learning models, to forecasting the fertility rate in the Kingdom of Saudi
Arabia, the data source is the World Health Organization over the period of
1960 to 2019. The performances of models were evaluated by errors measures
mean absolute percentage error.
Machine learning is the process of creating algorithms that extract useful facts from data automatically. The goal of this paper is to use an artificial neural network and a cubic spline model to predict various physical quantities displacement components in a thermoplastic solid, such as elastic waves, vector form, volume fraction field, thermal waves, stress components, and carrier density concentration (plasma waves). The mean absolute scaled error (MASE), the mean absolute percentage error (MAPE), and the symmetric mean absolute percentage errors (SMAPE) are used to compare the accuracy of two models. The true displacements are given their maximum expected values. These factors have also been described using various descriptive statistics and diagrams. Statistical significance was found in the examination of the correlation between the variables, and a comparison was conducted between the findings and prior results acquired by others. The findings show that voids, rotation, optical temperature, and thermal relaxation all have a significant impact on the phenomena, and they are in line with earlier physical findings. Furthermore, it is demonstrated that certain physical variables describing such systems may display this property, allowing for the development of an analytical criterion for the advent of dynamical chaos.
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